Mapping for Impact: Addressing Data Quality through Community Collaboration

In 2004, Steve Coast introduced OpenStreetMap (OSM), a platform designed to provide unrestricted geospatial data to a worldwide audience. OSM is a testament to global collaboration, where volunteers, whether armchair mappers or remote contributors, unite to enhance maps everywhere. Over time, OSM’s inherent nature has made it useful in a variety of applications over time, especially during humanitarian emergencies. OSM’s emphasis on community interaction is essential to its essence because it greatly depends on the cooperation of its contributors. Unlike government data with periodic updates, OSM allows even those without prior mapping skills to contribute, resulting in swift data growth, a necessity in a world where infrastructure is ever-expanding. Consequently, many use OSM for various purposes, while some still doubt its quality and accuracy.

The growth of OSM data undoubtedly offers numerous advantages through community collaboration. However, it comes at the cost of data quality, resulting in inconsistencies due to the absence of standardized mapping guidelines. These problems arise from various factors, including contributors lacking local knowledge or changes in the real world that have not been reflected in the maps. Consequently, the type of errors in OSM data varies from region to region. Indonesia, for instance, identified approximately 686,000 errors in its OSM data in the last six months, ranging from overlapping buildings to duplicate geometry. To rectify these issues and maintain data quality, Perkumpulan OpenStreetMap Indonesia (POI) and TomTom joined forces to advocate for the importance of OSM data quality in Indonesia.

Previously, TomTom launched MapRoulette challenges, an online OSM application that aims to solve OSM problems by creating challenges. They launched a challenge last year to fix highway mistakes in Indonesia. Still, the project ran into problems mainly because many local contributors in Indonesia were unfamiliar with MapRoulette and needed help understanding how to utilize it effectively.

From September 22 and 27, 2023, POI and TomTom organized an online event and competition named “Mapping for Impact,” which focused on rectifying OSM data errors. The event commenced on September 22 and began with an introduction emphasizing the significance of data quality, a demo session on MapRoulette, and the competition announcement. With little introductory information about OSM or how to map it, this event was more suited for experienced mappers. However, mappers of all skill levels happily participated. Forty six contributors in all took part in this event. The event proceeded smoothly, with participants highly motivated to correct the errors in OSM.

Following the competition, the POI and TomTom teams assessed the contributors’ contributions to determine the event’s winners. Collectively, 18 participants made a noteworthy 22,887 edits. We identified the top three contributors who had made the most edits and declared them as the winners on October 4, 2023, through an announcement on POI’s Instagram account. This award serves as a token of appreciation from POI and TomTom, recognizing the dedication and hard work of the community in enhancing the quality of OSM data.

Despite the fact that only 18 participants remained engaged until September 27th, their collaborative endeavors yielded a substantial impact. Preceding the event, Indonesia’s OSM data contained 163,172 items with overlapping issues and 247,341 buildings with overlapping concerns. Remarkably, over six days, they rectified more than 103,000 inaccuracies in their entirety. These figures highlight how well POI and TomTom’s partnership worked to provide favorable results and raise the possibility of improving data through outreach and education to the community. Furthermore, the OSM community’s reinforcement—especially from Indonesia’s local contributors—has had a major positive impact. The accomplishments of this collaboration highlight how group efforts can improve the accuracy and reliability of geospatial data for a wide range of applications.